code: def forward(self, input, target):
      centers = F.normalize(self.fc, p=2, dim=0)
      simInd = input.matmul(centers)
      simStruc = simInd.reshape(-1, self.cN, self.K)
      prob = F.softmax(simStruc * self.gamma, dim=2)
      simClass = torch.sum(prob * simStruc, dim=2)
      marginM = torch.zeros(simClass.shape, dtype=input.dtype).to(
          input.device
      )
      marginM[torch.arange(0, marginM.shape[0]), target] = self.margin
      lossClassify = F.cross_entropy(
          self.la * (simClass - marginM), target
      )
      if self.tau > 0 and self.K > 1:
          simCenter = centers.t().matmul(centers)
          small_val = c_f.small_val(input.dtype)
          simCenterMasked = torch.clamp(
              2.0 * simCenter[self.weight], max=2
          )
          reg = torch.sum(
              torch.sqrt(2.0 + small_val - simCenterMasked)
          ) / (self.cN * self.K * (self.K - 1.0))
          return lossClassify + self.tau * reg
      else:
          return lossClassify
  
file_path: pytorch-metric-learning/tests/losses/test_soft_triple_loss.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import init
  from torch.nn.parameter import Parameter
  from pytorch_metric_learning.utils import common_functions
  from ..zzz_testing_utils.testing_utils import angle_to_coord
  import unittest
  from pytorch_metric_learning.losses import SoftTripleLoss
  from pytorch_metric_learning.regularizers import SparseCentersRegularizer
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 2 for calling the OriginalImplementationSoftTriple obj's underlying member method code with example usage: obj.forward(input, target) and returns: unknown, lossClassify